A novel physics-informed framework for reconstruction of structural defects

Li, Qi, Liu, Fushun, Wang, Bin, Liu, Dianzi and Qian, Zhenghua (2022) A novel physics-informed framework for reconstruction of structural defects. Applied Mathematics and Mechanics-English Edition, 43 (11). 1717–1730. ISSN 0253-4827

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Abstract

The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propagation efficiency and low energy consumption during the inspect process. However, the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results. Besides, the scattering signals collected from industry sectors are often noised and nonstationary. To address these issues, a novel physics-informed framework (PIF) for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper. Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions, a deep-learning neural network model is built to reveal the physical relationship between the defects and the noisy detection signals. This learning model is then adopted to assess and characterize the defect profiles in structures, improve the accuracy of the analytical model, and eliminate the impact of the noise pollution in the process of inspection. To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising, several numerical examples are carried out. The results show that the PIF has greater accuracy for the reconstruction of defects in the structures than the analytical method, and provides a valuable insight into the development of artificial intelligence (AI)-assisted inspection systems with high accuracy and efficiency in the fields of structural integrity and condition monitoring.

Item Type:	Article
Additional Information:	Funding Information: Project supported by the National Natural Science Foundation of China (Nos. 12061131013, 12211530064, and 12172171), the Fundamental Research Funds for the Central Universities of China (Nos. NE2020002 and NS2019007), the National Natural Science Foundation of China for Creative Research Groups (No. 51921003), the Postgraduate Research and Practice Innovation Program of Jiangsu Province of China (No. KYCX21_0184), the National Natural Science Foundation of Jiangsu Province of China (No. BK20211176), the State Key Laboratory of Mechanics and Control of Mechanical Structures at Nanjing University of Aeronautics and Astronautics of China (No. MCMS-E-0520K02), and the Interdisciplinary Innovation Fund for Doctoral Students of Nanjing University of Aeronautics and Astronautics of China (No. KXKCXJJ202208).
Uncontrolled Keywords:	deep-learning,denoising,o343,physics-informed,reconstruction of defects,mechanics of materials,mechanical engineering,applied mathematics,sdg 7 - affordable and clean energy ,/dk/atira/pure/subjectarea/asjc/2200/2211
Faculty \ School:	Faculty of Science > School of Engineering (former - to 2024)
UEA Research Groups:	Faculty of Science > Research Groups > Sustainable Energy Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	12 Aug 2022 09:36
Last Modified:	07 Nov 2024 12:45
URI:	https://ueaeprints.uea.ac.uk/id/eprint/87196
DOI:	10.1007/s10483-022-2912-6

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